{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple\n",
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.5.1.tar.gz (35.3 MB)\n",
      "\u001B[K     |████████████████████████████████| 35.3 MB 9.4 kB/s eta 0:00:01    |▌                               | 512 kB 671 kB/s eta 0:00:52     |█████                           | 5.6 MB 1.9 MB/s eta 0:00:16     |█████▏                          | 5.7 MB 1.9 MB/s eta 0:00:16     |███████                         | 7.6 MB 1.9 MB/s eta 0:00:15     |█████████                       | 10.0 MB 1.9 MB/s eta 0:00:14     |████████████████                | 17.7 MB 2.9 MB/s eta 0:00:07     |████████████████▋               | 18.4 MB 3.2 MB/s eta 0:00:06     |███████████████████████▉        | 26.3 MB 1.2 MB/s eta 0:00:08     |██████████████████████████▎     | 29.0 MB 3.6 MB/s eta 0:00:02     |███████████████████████████▎    | 30.1 MB 3.6 MB/s eta 0:00:02     |████████████████████████████▊   | 31.8 MB 3.6 MB/s eta 0:00:01     |███████████████████████████████▊| 35.0 MB 254 kB/s eta 0:00:02\n",
      "\u001B[?25hCollecting cycler>=0.10\n",
      "  Downloading https://www.piwheels.org/simple/cycler/cycler-0.11.0-py3-none-any.whl (6.4 kB)\n",
      "Collecting fonttools>=4.22.0\n",
      "  Downloading https://www.piwheels.org/simple/fonttools/fonttools-4.28.3-cp39-cp39-linux_armv7l.whl (1.3 MB)\n",
      "\u001B[K     |████████████████████████████████| 1.3 MB 168 kB/s eta 0:00:01\n",
      "\u001B[?25hCollecting kiwisolver>=1.0.1\n",
      "  Downloading https://www.piwheels.org/simple/kiwisolver/kiwisolver-1.3.2-cp39-cp39-linux_armv7l.whl (1.1 MB)\n",
      "\u001B[K     |████████████████████████████████| 1.1 MB 642 kB/s eta 0:00:01\n",
      "\u001B[?25hRequirement already satisfied: numpy>=1.17 in /home/pi/envs/tordj/lib/python3.9/site-packages (from matplotlib) (1.21.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/pi/envs/tordj/lib/python3.9/site-packages (from matplotlib) (21.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/pi/envs/tordj/lib/python3.9/site-packages (from matplotlib) (8.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/pi/envs/tordj/lib/python3.9/site-packages (from matplotlib) (3.0.6)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/pi/envs/tordj/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/pi/envs/tordj/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Using legacy 'setup.py install' for matplotlib, since package 'wheel' is not installed.\n",
      "Installing collected packages: kiwisolver, fonttools, cycler, matplotlib\n",
      "    Running setup.py install for matplotlib ... \u001B[?25ldone\n",
      "\u001B[?25hSuccessfully installed cycler-0.11.0 fonttools-4.28.3 kiwisolver-1.3.2 matplotlib-3.5.1\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data=torch.Tensor([[1.],[2.],[3.]])\n",
    "y_data=torch.Tensor([[2.],[4.],[6.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearModel(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LinearModel,self).__init__()\n",
    "        self.linear=torch.nn.Linear(1,1)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        y_pred=self.linear(x)\n",
    "        return y_pred\n",
    "model=LinearModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pi/envs/tordj/lib/python3.9/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    }
   ],
   "source": [
    "criterion=torch.nn.MSELoss(size_average=False)\n",
    "optimizer=torch.optim.SGD(model.parameters(),lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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      "2438 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2439 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2440 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2441 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2442 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2443 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2444 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2445 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2446 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2447 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2448 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2449 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2450 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2451 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2452 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2453 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2454 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2455 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2456 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2457 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2458 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2459 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2460 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2461 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2462 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2463 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2464 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2465 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2466 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2467 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2468 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2469 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2470 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2471 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2472 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2473 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2474 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2475 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2476 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2477 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2478 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2479 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2480 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2481 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2482 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2483 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2484 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2485 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2486 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2487 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2488 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2489 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2490 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2491 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2492 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2493 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2494 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2495 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2496 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2497 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2498 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n",
      "2499 tensor(4.0927e-12, grad_fn=<MseLossBackward>)\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(2500):\n",
    "    y_pred=model(x_data)\n",
    "    loss=criterion(y_pred,y_data)\n",
    "    print(epoch,loss)\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=torch.Tensor([5.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.999995231628418\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    print(model(test_data).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path='./line.pkl'\n",
    "torch.save(model.state_dict(),model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_m=LinearModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_m.load_state_dict(torch.load(model_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearModel(\n",
       "  (linear): Linear(in_features=1, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_m.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.999995231628418\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    print(new_m(test_data).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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